Robust Framework for Detecting and Classifying Network-Based Attacks Using Ensemble Machine Learning Approach

Robust Framework for Detecting and Classifying Network-Based Attacks Using Ensemble Machine Learning Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Robust Framework for Detecting and Classifying Network-Based Attacks Using Ensemble Machine Learning Approach Rakhi Ashish Kalantri, Rajesh - Bansode This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6472892/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 5 You are reading this latest preprint version Abstract Cybersecurity threats are increasingly sophisticated, making it critical to design advanced systems for detecting and mitigating network-based attacks. However, existing approaches often face limitations in accuracy, scalability, and adaptability to diverse types of attacks. This research addresses these challenges by suggesting a robust model that combines advanced machine learning (ML) and optimization methods to deliver superior performance in detecting and classifying network-based attacks. The self-collected dataset of 7,000 instances with 26 features. The preprocessing stage includes duplicate record removal, missing value imputation, and feature normalization to ensure dataset consistency. Feature extraction incorporates statistical, network-based, and behavioral features, for identifying abnormal traffic patterns. A hybrid feature selection approach using the Zebra Optimization Algorithm (ZOA) and Pelican Optimization Algorithm (POA) is employed to enhance model performance for effective attack detection. This work introduces a network attack detection scheme utilizing Ensemble Machine Learning (EML) methods for enhanced detection of cybersecurity threats. Additionally, Prairie Dog Optimization (PDO), is used to optimize hyperparameters for superior model performance. The proposed framework demonstrates a high accuracy of 99.7%, recall of 98.4%, and precision of 99.4%. Thus, the outcomes establish the superior efficiency of the developed technique for efficient threat detection, offering a scalable and adaptable result for cybersecurity threats. Cybersecurity Network-Based Attacks Ensemble Machine Learning Hybrid Optimization Threat Detection Network Traffic Analysis Full Text Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Major revision 14 Oct, 2025 Reviewers agreed at journal 14 Sep, 2025 Reviewers invited by journal 14 Sep, 2025 Editor assigned by journal 22 Apr, 2025 First submitted to journal 22 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6472892","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514795519,"identity":"381edf9e-3f32-4b87-a356-a4a6cb2e71b7","order_by":0,"name":"Rakhi Ashish 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